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Gibbs Sampling with People
Peter Harrison · Raja Marjieh · Federico G Adolfi · Pol van Rijn · Manuel Anglada-Tort · Ofer Tchernichovski · Pauline Larrouy-Maestri · Nori Jacoby

Thu Dec 10 06:30 AM -- 06:45 AM (PST) @ Orals & Spotlights: Neuroscience

A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, while MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as ‘pleasantness’. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as ‘Gibbs Sampling with People’ (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (StyleGAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications.

Author Information

Peter Harrison (Max Planck Institute for Empirical Aesthetics)
Raja Marjieh (Max Planck Institute for Empirical Aesthetics)
Federico G Adolfi (Max-Planck Institute AE, Frankfurt, Germany)
Pol van Rijn (Max Planck Institute for Empirical Aesthetics)
Manuel Anglada-Tort (Max Planck Institute for Empirical Aesthetics)
Ofer Tchernichovski (Hunter College, CUNY)
Pauline Larrouy-Maestri (Max-Planck-Institute of Empircal Aesthetics)
Nori Jacoby (Max Planck Institute for Empirical Aesthetics)

I'm interested in exploring the role of culture in auditory perception, using iterated learning alongside classical psychophysical methods to characterize perceptual biases in music and speech rhythms in populations around the world. Other work has focused on the mathematical modeling of sensorimotor synchronization in the form of tapping experiments as well as the application of machine-learning techniques to model aspects of musical syntax, including tonal harmony, birdsong, and the perception of musical form. I am currently a Research Group Leader at the Max Planck Institute for Empirical Aesthetics in Frankfurt, where I direct the "Computational Auditory Perception" group. Previously, I was a Presidential Scholar In Society And Neuroscience at Columbia University, a postdoc at the McDermott Computational Audition Lab at MIT, and a visiting postdoctoral researcher in Tom Griffiths's Computational Cognitive Science Lab at Berkeley. I completed my Ph.D. at the Edmond and Lily Safra Center for Brain Sciences (ELSC) at the Hebrew University of Jerusalem under the supervision of Naftali Tishby and Merav Ahissar, and hold a M.A. in mathematics from the same institution. My research has been published in journals including Current Biology, Science, Nature, Nature Scientific Reports, Philosophical Transactions B, Journal of Neuroscience, Journal of Vision, and Psychonomic Bulletin and Review.

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